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Record W3186344031 · doi:10.1145/3450626.3459842

Optimizing UI layouts for deformable face-rig manipulation

2021· article· en· W3186344031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Graphics · 2021
Typearticle
Languageen
FieldComputer Science
TopicFace recognition and analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceFace (sociological concept)Computer graphics (images)SliderInterface (matter)Object (grammar)Human–computer interactionEngineering drawingComputer visionArtificial intelligenceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Complex deformable face-rigs have many independent parameters that control the shape of the object. A human face has upwards of 50 parameters (FACS Action Units), making conventional UI controls hard to find and operate. Animators address this problem by tediously hand-crafting in-situ layouts of UI controls that serve as visual deformation proxies, and facilitate rapid shape exploration. We propose the automatic creation of such in-situ UI control layouts. We distill the design choices made by animators into mathematical objectives that we optimize as the solution to an integer quadratic programming problem. Our evaluation is three-fold: we show the impact of our design principles on the resulting layouts; we show automated UI layouts for complex and diverse face rigs, comparable to animator handcrafted layouts; and we conduct a user study showing our UI layout to be an effective approach to face-rig manipulation, preferable to a baseline slider interface.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.270
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it